A. Zheng, Michael I. Jordan, B. Liblit, M. Naik, A. Aiken
{"title":"统计调试:同时识别多个bug","authors":"A. Zheng, Michael I. Jordan, B. Liblit, M. Naik, A. Aiken","doi":"10.1145/1143844.1143983","DOIUrl":null,"url":null,"abstract":"We describe a statistical approach to software debugging in the presence of multiple bugs. Due to sparse sampling issues and complex interaction between program predicates, many generic off-the-shelf algorithms fail to select useful bug predictors. Taking inspiration from bi-clustering algorithms, we propose an iterative collective voting scheme for the program runs and predicates. We demonstrate successful debugging results on several real world programs and a large debugging benchmark suite.","PeriodicalId":124011,"journal":{"name":"Proceedings of the 23rd international conference on Machine learning","volume":"31 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2006-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"168","resultStr":"{\"title\":\"Statistical debugging: simultaneous identification of multiple bugs\",\"authors\":\"A. Zheng, Michael I. Jordan, B. Liblit, M. Naik, A. Aiken\",\"doi\":\"10.1145/1143844.1143983\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We describe a statistical approach to software debugging in the presence of multiple bugs. Due to sparse sampling issues and complex interaction between program predicates, many generic off-the-shelf algorithms fail to select useful bug predictors. Taking inspiration from bi-clustering algorithms, we propose an iterative collective voting scheme for the program runs and predicates. We demonstrate successful debugging results on several real world programs and a large debugging benchmark suite.\",\"PeriodicalId\":124011,\"journal\":{\"name\":\"Proceedings of the 23rd international conference on Machine learning\",\"volume\":\"31 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2006-06-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"168\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 23rd international conference on Machine learning\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/1143844.1143983\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 23rd international conference on Machine learning","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/1143844.1143983","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Statistical debugging: simultaneous identification of multiple bugs
We describe a statistical approach to software debugging in the presence of multiple bugs. Due to sparse sampling issues and complex interaction between program predicates, many generic off-the-shelf algorithms fail to select useful bug predictors. Taking inspiration from bi-clustering algorithms, we propose an iterative collective voting scheme for the program runs and predicates. We demonstrate successful debugging results on several real world programs and a large debugging benchmark suite.
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